Data Fusion for Errors-in-Variables
测量误差问题中的数据融合AI 解读 ↓
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测量误差问题中的数据融合
- 研究问题
- 目标样本仅有一次含误差测量，而外部异质人群提供重复测量时，如何识别测量误差并估计目标总体的函数量？
- 方法与思路
- 作者结合条件可迁移性与重复测量误差条件实现识别，再以条件反卷积、灵活辅助估计和正交修正构造融合估计量。
- 材料陈述的贡献
- 摘要称建立了覆盖两类谱目标的统一理论、相合性与收敛率，并在额外中心化条件下给出有限原子谱目标的推断结果。
- 阅读关注点
- 应核对可迁移性和重复误差条件的实质限制，以及不同目标的谱性质、中心化要求与自助法有效性的关系。
- 阅读边界
- 这是 arXiv v1 预印本，不能据此声称已同行评审或期刊接收；摘要未给出完整识别条件、收敛率公式及应用细节。
原文摘要
We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an external source study provides repeated surrogate measurements from a different population. The measurement error distribution is allowed to depend on the observed error-free variables, and the error-free variable distribution itself may differ between studies. We introduce a conditional transportability assumption that enables the use of external repeated measurements under source-target heterogeneity. Together with additional replicate-error conditions, it identifies the target conditional measurement-error distribution. Building on this identification result, we develop a data-fusion estimator for a broad class of target functionals. The estimator combines conditional deconvolution, flexible nuisance estimation, and orthogonal correction that reduces first-order sensitivity to nuisance estimation. For the proposed estimator, we develop a unified spectral theory covering both diffuse-spectrum and finite atomic-spectrum target functionals, derive a general asymptotic expansion, and establish consistency and target-specific convergence-rate bounds. The resulting convergence-rate bounds depend jointly on the spectral properties of the measurement error, the latent exposure, and the target functional. For finite atomic-spectrum targets, we further establish joint Gaussian and bootstrap limits, yielding inference for smooth moment transformations under an additional centering condition. In the reported simulations, Fuse-EIV has small bias for the primary exposure-related coefficient. Applications to the National Health and Nutrition Examination Survey illustrate how accounting for population heterogeneity and error heteroscedasticity can change empirical conclusions.